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Article: Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity

TitleDeep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity
Authors
Issue Date2020
PublisherRoyal Society of Chemistry. The Journal's web site is located at http://www.rsc.org/loc
Citation
Lab On a Chip, 2020, v. 20, p. 3696-3708 How to Cite?
AbstractThe association of the intrinsic optical and biophysical properties of cells to homeostasis and pathogenesis has long been acknowledged. Defining these label-free cellular features obviates the need for costly and time-consuming labelling protocols that perturb the living cells. However, wide-ranging applicability of such label-free cell-based assays requires sufficient throughput, statistical power and sensitivity that are unattainable with current technologies. To close this gap, we present a large-scale, integrative imaging flow cytometry platform and strategy that allows hierarchical analysis of intrinsic morphological descriptors of single-cell optical and mass density within a population of millions of cells. The optofluidic cytometry system also enables the synchronous single-cell acquisition of and correlation with fluorescently labeled biochemical markers. Combined with deep neural network and transfer learning, this massive single-cell profiling strategy demonstrates the label-free power to delineate the biophysical signatures of the cancer subtypes, to detect rare populations of cells in the heterogeneous samples (10–5), and to assess the efficacy of targeted therapeutics. This technique could spearhead the development of optofluidic imaging cell-based assays that stratify the underlying physiological and pathological processes based on the information-rich biophysical cellular phenotypes.
Persistent Identifierhttp://hdl.handle.net/10722/293210
ISSN
2020 Impact Factor: 6.799
2015 SCImago Journal Rankings: 2.373
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSIU, DMD-
dc.contributor.authorLee, KCM-
dc.contributor.authorLo, MCK-
dc.contributor.authorStassen, SV-
dc.contributor.authorWang, M-
dc.contributor.authorZhang, IZQ-
dc.contributor.authorSo, HKH-
dc.contributor.authorChan, GCF-
dc.contributor.authorCheah, KSE-
dc.contributor.authorWong, KKY-
dc.contributor.authorHsin, MKY-
dc.contributor.authorHo, JCM-
dc.contributor.authorTsia, KK-
dc.date.accessioned2020-11-23T08:13:25Z-
dc.date.available2020-11-23T08:13:25Z-
dc.date.issued2020-
dc.identifier.citationLab On a Chip, 2020, v. 20, p. 3696-3708-
dc.identifier.issn1473-0197-
dc.identifier.urihttp://hdl.handle.net/10722/293210-
dc.description.abstractThe association of the intrinsic optical and biophysical properties of cells to homeostasis and pathogenesis has long been acknowledged. Defining these label-free cellular features obviates the need for costly and time-consuming labelling protocols that perturb the living cells. However, wide-ranging applicability of such label-free cell-based assays requires sufficient throughput, statistical power and sensitivity that are unattainable with current technologies. To close this gap, we present a large-scale, integrative imaging flow cytometry platform and strategy that allows hierarchical analysis of intrinsic morphological descriptors of single-cell optical and mass density within a population of millions of cells. The optofluidic cytometry system also enables the synchronous single-cell acquisition of and correlation with fluorescently labeled biochemical markers. Combined with deep neural network and transfer learning, this massive single-cell profiling strategy demonstrates the label-free power to delineate the biophysical signatures of the cancer subtypes, to detect rare populations of cells in the heterogeneous samples (10–5), and to assess the efficacy of targeted therapeutics. This technique could spearhead the development of optofluidic imaging cell-based assays that stratify the underlying physiological and pathological processes based on the information-rich biophysical cellular phenotypes.-
dc.languageeng-
dc.publisherRoyal Society of Chemistry. The Journal's web site is located at http://www.rsc.org/loc-
dc.relation.ispartofLab On a Chip-
dc.titleDeep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity-
dc.typeArticle-
dc.identifier.emailLee, KCM: cmleehku@connect.hku.hk-
dc.identifier.emailStassen, SV: shobana@hku.hk-
dc.identifier.emailWang, M: ma01in@hku.hk-
dc.identifier.emailSo, HKH: hso@eee.hku.hk-
dc.identifier.emailChan, GCF: gcfchan@hku.hk-
dc.identifier.emailCheah, KSE: hrmbdkc@hku.hk-
dc.identifier.emailWong, KKY: kywong@eee.hku.hk-
dc.identifier.emailHo, JCM: jhocm@hku.hk-
dc.identifier.emailTsia, KK: tsia@hku.hk-
dc.identifier.authoritySo, HKH=rp00169-
dc.identifier.authorityChan, GCF=rp00431-
dc.identifier.authorityCheah, KSE=rp00342-
dc.identifier.authorityWong, KKY=rp00189-
dc.identifier.authorityHo, JCM=rp00258-
dc.identifier.authorityTsia, KK=rp01389-
dc.description.naturelink_to_subscribed_fulltext-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1039/D0LC00542H-
dc.identifier.pmid32935707-
dc.identifier.scopuseid_2-s2.0-85092945191-
dc.identifier.scopuseid_2-s2.0-85092945191-
dc.identifier.hkuros318748-
dc.identifier.volume20-
dc.identifier.spage3696-
dc.identifier.epage3708-
dc.identifier.isiWOS:000577744000013-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl1473-0189-

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